Car Accident Detection Detection Using Real Time CCTV: Deep Learning Approach

Abstract

The exponential growth of urbanization and vehicular traffic necessitates advanced solutions for ensuring road safety and minimizing the impact of accidents. In this paper, we present a comprehensive study on real-time car accident detection leveraging state-of-the-art Convolutional Neural Networks (CNNs) applied to CCTV footage. By harnessing the power of deep learning, our proposed approach aims to address the critical need for timely detection and response to traffic incidents, ultimately saving lives and reducing the economic and social costs associated with road accidents. Our methodology involves preprocessing raw CCTV footage to extract relevant frames capturing traffic scenes. These frames are then fed into a CNN architecture designed to learn discriminative features indicative of car accidents. Through extensive experimentation on a large-scale dataset encompassing diverse traffic scenarios and accident types, we demonstrate the effectiveness of our CNN-based model in achieving an outstanding accuracy rate of 98.